Month: October 2023

We are neither inventors nor visionaries of what insurance and risk management will look like, but we are avid students who seek out and listen closely to what those who make markets and steer ships say, do, and suggest about the future. With discernment and a healthy collection of data as a background, here is what we predict you can look forward to.

• Granular data about you will be more involved in determining what insurance is available to you and at what price. Your daily habits and behaviors (and, in some cases, in real-time) relative to your risk hygiene will govern your insurance profile. This isn’t much of a prediction as this is already here, but it will be the norm, not the exception.
• Insurance coverages will be unbundled such that the coverage you’ll get for fire risks might be separate from the coverage you’ll get for theft risks, which might be separate from what you’ll get for water risks.
• Subscription services that combine proactive mitigation services with loss recovery help will come from data companies who’ll fight for profit based on the losses you don’t have.
• Reduced mass-market insurance options and increased specialty insurance options.
• A smaller number of people selling you insurance, a greater number of people connecting you to a broader ecology of services that aim to reduce your demand for ever needing to use insurance.

Many questions remain about how deploying new methods reaches a critical mass for fundamental change. In addition, fairness, transparency, and trustworthiness have not yet been measured sufficiently to shepherd a path toward all of this change being deemed “good”. But these questions are already in the minds of important people, and I have confidence that the effort to answer them will lead to a new and better way than how all sides would describe the current state of insurance in the U.S.

I am passionate about, and excited for these things coming to the world of risk and uncertainty management:

• Widely available alternatives to traditional insurance underwriting, pricing, and products.
• Equalized access to loss-prevention data so consumers can be guided toward desired outcomes.
• Consumer-centricity. Even an attempt at it.
• AI that synthesizes data with less bias than decisions made by what people “feel is true”.
• A commitment to being a social good.
• Replacement of the Morse-code like communications experience with insurance companies and their systems.
• An end to blaming losses for failure when it is the precise nature of the business you’re in.
• Predict & prevent > respond & recover.
• Some optimism. About anything.

Revolutions start when order turns to disorder. Eventually, reorder becomes a preferred replacement to the old ways of doing things. Traditional insurance and risk ecologies are demonstrating systemic weaknesses highlighting how quickly legacy belief systems can become uncompetitive. The technology industry is especially poised to introduce themselves as replacements to those who reverse-engineer response plans based on irrelevant history. I don’t know if they’ll wind up doing better, but it’s time to give them a try.